US20230395206A1 - Information processing method, information processing apparatus, and program - Google Patents

Information processing method, information processing apparatus, and program Download PDF

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US20230395206A1
US20230395206A1 US18/247,663 US202018247663A US2023395206A1 US 20230395206 A1 US20230395206 A1 US 20230395206A1 US 202018247663 A US202018247663 A US 202018247663A US 2023395206 A1 US2023395206 A1 US 2023395206A1
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Prior art keywords
user
information processing
question
information
processing apparatus
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Inventor
Tomu TOMINAGA
Takeshi Kurashima
Hiroyuki Toda
Shuhei Yamamoto
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NTT Inc USA
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Nippon Telegraph and Telephone Corp
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Assigned to NIPPON TELEGRAPH AND TELEPHONE CORPORATION reassignment NIPPON TELEGRAPH AND TELEPHONE CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KURASHIMA, TAKESHI, TODA, HIROYUKI, TOMINAGA, Tomu, YAMAMOTO, SHUHEI
Publication of US20230395206A1 publication Critical patent/US20230395206A1/en
Assigned to NTT, INC. reassignment NTT, INC. CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present disclosure relates to an information processing method, an information processing apparatus, and a program.
  • EMA Electronicological Momentary Assessment
  • the EMA is implemented by transmitting a question to a user via a mobile terminal or the like in a specific time period and obtaining a response from the user.
  • maximization of the number V of answers in an EMA study refers to maximization of the response rate R_(i, d, k).
  • the EMA study in the related art adopts a strategy of transmitting EMA questionnaires to users a predetermined number of times in a specific time period. Depending on a length of the experiment period, the EMA questionnaires are often transmitted once or several times a day (for example, refer to Non Patent Literature 1).
  • the response rate may be low.
  • an object is to provide a technique capable of improving the response rate.
  • an information processing apparatus executes transmitting information related to a question for a user to a terminal of the user at a timing according to information indicating a characteristic of the user, information indicating an experience of the user, and a history of responses by the user.
  • the response rate can be improved.
  • FIG. 1 is a diagram illustrating a configuration of a communication system according to an embodiment.
  • FIG. 2 is a diagram illustrating a hardware configuration example of an information processing apparatus according to the embodiment.
  • FIG. 3 is a diagram illustrating an example of a configuration of the information processing apparatus according to the embodiment.
  • FIG. 4 is a flowchart illustrating an example of model generation processing of the information processing apparatus according to the embodiment.
  • FIG. 5 A is a diagram illustrating an example of an individual characteristic database according to the embodiment.
  • FIG. 5 B is a diagram illustrating an example of an experience database according to the embodiment.
  • FIG. 5 C is a diagram illustrating an example of a response history database according to the embodiment.
  • FIG. 6 is a flowchart illustrating an example of estimation processing of the information processing apparatus according to the embodiment.
  • FIG. 1 is a diagram illustrating a configuration of a communication system 1 according to an embodiment.
  • the communication system 1 includes an information processing apparatus 10 , a terminal 20 A, a terminal 20 B, and a terminal 20 C.
  • the terminal 20 A, the terminal 20 B, and the terminal 20 C are also simply referred to as “terminal 20 ” in a case where it is not necessary to distinguish the terminals.
  • the numbers of the information processing apparatuses 10 and the terminals 20 are not limited to the example of FIG. 1 .
  • the information processing apparatus 10 and the terminal 20 perform communication with each other via, for example, a network N such as a mobile communication network such as 5th generation mobile communication system (5G), 4G, long term evolution (LTE), or 3G, a wireless local area network (LAN), or the Internet.
  • a network N such as a mobile communication network such as 5th generation mobile communication system (5G), 4G, long term evolution (LTE), or 3G, a wireless local area network (LAN), or the Internet.
  • the information processing apparatus 10 is, for example, an information processing apparatus such as a server.
  • the information processing apparatus 10 transmits an EMA questionnaire (an example of “question”) to the terminal 20 .
  • the information processing apparatus 10 receives a response to the EMA questionnaire from the terminal 20 of a user.
  • the terminal 20 is a terminal used by a user.
  • the terminal 20 may be, for example, a terminal such as a smartphone, a tablet, a personal computer, or a wearable device.
  • FIG. 2 is a diagram illustrating a hardware configuration example of the information processing apparatus 10 according to the embodiment.
  • the information processing apparatus 10 includes a drive device 1000 , an auxiliary storage device 1002 , a memory device 1003 , a CPU 1004 , an interface device 1005 , and the like, which are connected to each other via a bus B.
  • An information processing program for implementing processing in the information processing apparatus 10 may be provided by a recording medium 1001 .
  • the information processing program in a case where the recording medium 1001 in which the information processing program is recorded is set in the drive device 1000 , the information processing program is installed from the recording medium 1001 to the auxiliary storage device 1002 via the drive device 1000 .
  • the information processing program is not necessarily installed from the recording medium 1001 , and may be downloaded from another computer via a network.
  • the auxiliary storage device 1002 stores the installed information processing program, and also stores necessary files, data, and the like.
  • the memory device 1003 reads the program from the auxiliary storage device 1002 and stores the program.
  • the CPU 1004 executes processing according to the program stored in the memory device 1003 .
  • the interface device 1005 is used as an interface for connection to the network.
  • examples of the recording medium 1001 include portable recording mediums such as a CD-ROM, a DVD disk, or a USB memory. Further, examples of the auxiliary storage device 1002 include a hard disk drive (HDD), a flash memory, and the like. Each of the recording medium 1001 and the auxiliary storage device 1002 corresponds to a computer-readable recording medium.
  • portable recording mediums such as a CD-ROM, a DVD disk, or a USB memory.
  • examples of the auxiliary storage device 1002 include a hard disk drive (HDD), a flash memory, and the like.
  • the information processing apparatus 10 may be implemented by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).
  • ASIC application specific integrated circuit
  • FPGA field-programmable gate array
  • FIG. 3 is a diagram illustrating an example of the configuration of the information processing apparatus 10 according to the embodiment.
  • the information processing apparatus 10 includes a storage unit 11 , an acquisition unit 12 , a generation unit 13 , an estimation unit 14 , and a notification unit 15 . These units may be implemented by cooperation of one or more programs installed in the information processing apparatus 10 and hardware such as the CPU 1004 of the information processing apparatus 10 .
  • the storage unit 11 stores various types of information.
  • the storage unit 11 includes, for example, an individual characteristic database 111 that stores individual characteristic data as information indicating a characteristic of a user, an experience database 112 that stores experience data as information indicating an experience of a user, a response history database 113 that stores a response history as a history of a response to a question by a user, and the like.
  • the acquisition unit 12 acquires various types of information, and stores the information in the storage unit 11 .
  • the acquisition unit 12 records the experience data received from the terminal 20 in the experience database 112 .
  • the acquisition unit 12 records, for example, information related to the response received from the terminal 20 , in the response history database 113 .
  • the generation unit 13 generates a model for estimating a response rate (answer rate) of a user based on the information stored in the storage unit 11 .
  • the estimation unit 14 estimates a response rate of a user based on the information stored in the storage unit 11 and the model generated by the generation unit 13 .
  • the notification unit 15 transmits a question to the terminal 20 of the user at a timing when the response rate of the user estimated by the estimation unit 14 satisfies a predetermined condition. Further, the notification unit 15 transmits, to the terminal 20 of the user, a reminder for a response to the question at a timing when the response rate of the user estimated by the estimation unit 14 satisfies a predetermined condition.
  • FIG. 4 is a flowchart illustrating an example of model generation processing of the information processing apparatus 10 according to the embodiment.
  • FIG. 5 A is a diagram illustrating an example of the individual characteristic database 111 according to the embodiment.
  • FIG. 5 B is a diagram illustrating an example of the experience database 112 according to the embodiment.
  • FIG. 5 C is a diagram illustrating an example of the response history database 113 according to the embodiment. Note that the information processing apparatus 10 may execute processing illustrated in FIG. 4 , for example, at a predetermined cycle.
  • step S 101 the acquisition unit 12 of the information processing apparatus 10 acquires learning data for generating the model to estimate the response rate of each user from the individual characteristic database 111 , the experience database 112 , and the response history database 113 of the storage unit 11 .
  • individual characteristic data is recorded in the individual characteristic database 111 in association with a user ID.
  • the user ID is identification information of the user of the terminal 20 .
  • the individual characteristic data may include, for example, pieces of information indicating a personality (character), a mental state, a taste, a gender, an age, a job type, and the like.
  • the information recorded in the individual characteristic database 111 may be registered in advance based on, for example, a questionnaire or a Questionnaire survey performed in the terminal 20 or the like.
  • the acquisition unit 12 acquires N items of individual characteristic data P i 1 , P i 2 , . . . , and P i N included in the individual characteristic data of a user i (i is an integer of 1 or more), as multi-stage evaluation values.
  • N indicates an integer of 1 or more. Note that the number of stages of the evaluation values of each item included in the individual characteristic data may be the same or different.
  • experience data is recorded in the experience database 112 in association with a set of a user ID and an experience date and time.
  • the experience date and time is a date and time when an experience related to the experience data occurs.
  • the experience date and time may be, for example, a date and time when the experience data is acquired by the information processing apparatus 10 .
  • the experience data may include information acquired by a sensor of the terminal 20 of the user.
  • the sensor may include, for example, a microphone sensor, a depth sensor, an optical sensor, an acceleration sensor, a temperature sensor, a global positioning system (GPS) sensor, a camera sensor, and the like. Further, the sensor may include, for example, various sensors provided on a digital device.
  • GPS global positioning system
  • the experience data may include, for example, an accumulated time or the like of a conversation of the user that is analyzed based on a voice collected by the microphone of the terminal 20 .
  • the tendency can be used.
  • the experience data may include, for example, an accumulated time or the like of an exercise of the user that is analyzed based on acceleration collected by an acceleration sensor of the terminal 20 .
  • the tendency can be used.
  • the experience data may include a behavior history of the user in the terminal 20 of the user.
  • the behavior history may include, for example, a transmission/reception history of messages by a social networking service (SNS), an e-mail, and the like, a browsing history of a specific website, and the like.
  • SNS social networking service
  • the tendency can be used in a state where a questionnaire is transmitted while the user is exchanging messages or shopping online on an electronic commerce (EC) site. Further, for example, in a state where a questionnaire is transmitted while the user is browsing a specific news site or the like, in a case where there is a tendency that the response rate increases, the tendency can be used.
  • EC electronic commerce
  • the acquisition unit 12 acquires, as time-series logs, most recent (in chronological order) M items of experience logs E i,d,t 1 , E i,d,t 2 , . . . , E i,d,t M that are included in the experience log.
  • M indicates an integer of 1 or more.
  • the experience log E i,d,t m of the m-th item includes pieces of measurement data for n timing points that are observed between a timing t-h and a timing t.
  • the experience log E i,d,t m is expressed as a vector as in the following Equation (2).
  • the number of elements (the number of times of measurement) of the vector of each item included in the experience log may be the same or different. Further, units (turns, times, degrees, bpm, and the like) of each item included in the experience log may be different.
  • response history data is recorded in the response history database 113 in association with a set of a user ID and a question ID.
  • the question ID is identification information of a question (EMA questionnaire) transmitted to the terminal 20 by the information processing apparatus 10 .
  • the response history data includes a timing (transmission timing) at which the question related to the question ID is transmitted to the terminal 20 of the user related to the user ID, the presence or absence of a response to the question from the user, and a timing (response timing) at which a response to the question from the user is received.
  • the generation unit 13 of the information processing apparatus 10 generates a model for estimating a response rate of each user based on the learning data acquired by the acquisition unit 12 (step S 102 ).
  • the generation unit 13 may define an estimation value R i,d,k of a maximum value of the response rate in a time period [T 0 , T 1 ] by the following Equation (3).
  • the individual characteristic data of the user is P i
  • that a specific question performed on a date d is k
  • that a time period in which the k is performed is [T 0 , T 1 ]
  • that latest experience data from a certain timing t-h to a timing t is E i,d,t
  • a function (response rate estimation function) representing (describing) a relationship between the response rate and the individual characteristic and the latest experience is f.
  • ⁇ indicates a parameter set.
  • the generation unit 13 can estimate (derive) a temporal change R i,d,k (t) of the response rate from a timing T 0 to a timing T 1 by using the individual characteristic data P i and the latest experience data E i,d,t according to 8.
  • the generation unit 13 may determine the response rate estimation function f as a model for estimating the response rate of each user by the following processing. First, the generation unit 13 models, by machine learning or the like, a correspondence relationship among the individual characteristic data, the latest experience data, the presence or absence of a response to a question, a Question transmission timing, and a difference between the question transmission timing and the response timing (an elapsed time from transmission of the question to reception of the response).
  • the generation unit 13 may determine (define) a response rate to, for example, a certain EMA questionnaire k as in the following Equation (4).
  • the generation unit 13 may determine (define) a response rate R ⁇ circumflex over ( ) ⁇ i,d,k estimated based on, for example, the individual characteristic data P i , the latest experience log E i,d,t , and the parameter set ⁇ as in the following expression (5).
  • the generation unit 13 may calculate (derive) a solution to an optimization problem of the following Equation (6) based on Equation (4) and Equation (5)
  • the generation unit 13 generates (determines, configures) a response rate estimation function f, which is a model for estimating the response rate of each user, by the parameter set ⁇ calculated from Equation (6).
  • FIG. 6 is a flowchart illustrating an example of estimation processing of the information processing apparatus 10 according to the embodiment.
  • the information processing apparatus 10 may execute processing illustrated in FIG. 6 at a predetermined cycle after a new question is registered by, for example, an operator or the like of the information processing apparatus 10 .
  • step S 201 the acquisition unit 12 of the information processing apparatus 10 acquires estimation data for estimating the response rate of each user from the individual characteristic database 111 and the experience database 112 of the storage unit 11 .
  • the acquisition unit 12 acquires, from the individual characteristic database 111 , N items of individual characteristic data P i 1 , P i 2 , . . . , and P i N included in the individual characteristic data of a user i, as multi-stage evaluation values.
  • the acquisition unit 12 acquires, from the experience database 112 , as time-series logs, most recent (in chronological order) M items of experience logs E i,d,t 1 , E i,d,t 2 , . . . , E i,d,t M that are included in the experience log.
  • the estimation unit 14 of the information processing apparatus 10 estimates the response rate of each user based on the model generated by the generation unit 13 in the above-described processing of FIG. 4 and the estimation data acquired by the acquisition unit 12 (step S 202 ).
  • the estimation unit 14 may estimate a timing s at which the response rate is maximized from the timing T 0 to the timing T 1 by substituting pieces of data acquired from the individual characteristic database 111 and the experience database 112 by the acquisition unit 12 into the response rate estimation function f according to the following Equation (7).
  • the timing T 0 and the timing T 1 in the estimation processing of FIG. 6 are timings after the timing T 1 described in the model generation processing of FIG. 4 .
  • the timing T 0 and the timing T 1 in Equation (2), Equation (5), and Equation (7) and the like in the estimation processing of FIG. 6 may be respectively read as a timing T 2 , a timing T 3 , and the like.
  • the notification unit 15 of the information processing apparatus 10 transmits information related to the question to the terminal 20 at a timing when the response rate estimated by the estimation unit 14 satisfies a predetermined condition (step S 203 ).
  • the notification unit 15 may transmit information related to the question to the terminal 20 , for example, at a timing s at which the response rate estimated by the estimation unit 14 is maximized.
  • the information related to the question may be, for example, data of the question itself, a uniform resource locator (URL) of a website or the like on which the question can be browsed, or the like. Further, the information related to the question may be a reminder message or the like that prompts a response to the question which is already transmitted.
  • URL uniform resource locator
  • the notification unit 15 may perform notification by, for example, an email to a mobile terminal possessed by the user, a notification or a reminder using a function of an application or an operating system (OS) of the mobile terminal, or the like.
  • OS operating system
  • the generation unit 13 may generate a model for estimating the response rate of each user by, for example, a machine learning method such as a neural network (NN).
  • a machine learning method such as a neural network (NN).
  • At least a part of the functional units of the information processing apparatus 10 may be implemented by, for example, cloud computing provided by one or more computers.
  • the storage unit 11 , the generation unit 13 , and the like may be provided in an external information processing apparatus.
  • the response rate of the user during an experiment period depends on a reward amount and a difficulty level of the question and is constant with respect to time.
  • the response rate of the user during a certain period is not constant with respect to time. For example, it is considered that a motivation to answer a question increases and the response rate increases at a certain timing and that a motivation to answer a question decreases and the response rate decreases at a certain timing due to an event experienced by a person in daily life.
  • the response rate of the user is estimated, and intervention on the user is performed at a timing when the response rate of the user is estimated to be high. Thereby, for example, the response rate can be improved.

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